Data Monetization & Sharing Data

Facts and Figures

  • According to the Sloan School of Management, data monetization is all about leveraging the data that you have through new channels.
  • Channels for Data Monetization:

They can be data or insights, like:

  • A potential revenue increase of 10% for companies that employ advanced digital technologies and customer data to build personalized experiences.
  • Businesses lose 300 bill $ annually, on a global scale, because of poor customer experience (200 bill $ lost to competitors).
  • For 80% of consumers, personally relevant content (notifications, offers, etc.) inclines them to purchase.
  • Over 50% of consumers value an all-digital-channels personalized experience within a brand.
  • –i.g.: a restaurant chain that wants to know where its clients shop before and after visiting its restaurants–.
    • These data can be classified into three types of data, according to their risk level:
      • High riskraw data: customer’s name, credit card number, address, contacts, date of birth, time of purchase and cost, etc.
      • Medium risk – anonymized data: does not identify the customer.
      • Low risk – synthetic data: a falsified data set created from the core data –e.g.: slightly changed numbers that make the transaction irreversible–.

Artificial Intelligence (AI) products –such as chat bots, digital assistants, self-driving cars, or healthcare innovation cures–.

  • Examples:
    • Beazley (insurance company) joined with TRA Stanford to improve patient security at the hospitals it insures.
    • Allianz Global Corporate and Specialty partnered with Praedicat to aid its customers in improving their handling of chemicals.  
    • XL Catlin is working with the Driven autonomous vehicle consortium in the UK to determine the risks from self-driving vehicles.
    • Accenture and Pitney Bowes developed the Property Evaluator –which evaluates 100 risk property characteristics that need insurance— increasing the rapidity and precision of property underwriting.
    • Accenture has developed Vector4D: it helps insurers use their data more effectively and control property risks to aid customers when natural disasters occur.

Sources: The above text is a creative synthesis elaborated from the following sources: Zachary Mack (The Verge); John Cusano (; John Nash (REDPOINTGlobal).

These sources have been selected from a total of 15 articles on the subject matter. Which in turn are the result of sifting through 99 articles.


  • To monetize data, companies need a single point of control:
    • Multiple channels create distrust from customers.
    • And difficulties identifying the customer across all channels.
  • Companies are accountable for protecting and managing personal data, but the real owner is the person or company referred to by the data.
  • Partnerships can be critical in the monetization of data sources:
    • Data by itself becomes a valuable economic asset when some other company is willing to pay for it.
  • Data language:
    • Successful Digital Transformation cannot be done if a company does not “speak data language” within itself and with its clients.
    • Data language applied to the industrial sector, for example, will bring the following benefits, according to Vinay Nathan, IIoT World:
      • More real-time transparency: for example, remote monitoring of assets (solar panels, field water pumps, gas pipelines, energy meters, etc.) enables managers to be proactive.
      • Better employee collaboration: more lucid information flows enable managers and engineers to react immediately to changing production needs.
      • Increased productivity & improved equipment effectiveness: minimum human intervention leads to machine-based infrastructure, thus increasing the equipment efficiency and decreasing downtime risk.
      • Better market agility: condition-based monitoring and predictive maintenance in such industries as automotive or tire facilities are the basis for data-driven decisions, predictions and proactive responses.
      • Better customer experience: feedback from the customer is received continuously as he/she interacts with the company (via social media, through the very products and services…), is analyzed to draw insights that drive decisions to re-design or improve products or services.
  • Main marketers’ mistake: creating too many channels and not being able to recognize their customers at the point of interaction.
  • Major challenge: to have that data analyzed by third parties without personal data being leaked.
  • Open Data allows for the discovery of meaningful insights and the creation of new data sets, that can be used by everyone.
  • Open Data provides benefits to organizations, governments and individuals:
    • As they represent reliable, free and pre-processed data about demography, society, economy, environment or public sector,
  • For example, in Spain:
  • Anheuser-Busch (AB InBev) is the world’s largest brewing company:
    • Business estimated at 55 bill $, which includes 500 beer brands in 100 countries.
    • The company’s goal is a 60 bill $ – 100 bill $ business.
    • To reach it, AB InBev introduced a data monetization strategy that included a worldwide data platform’s assessment, design, architecture and delivery.
  • The company identified thousands of local systems and data silos that have developed organically by country and geographic market.
  • The company built a single unified platform standardized and rationalized across data sources, regions and brands.
  • The platform provides a foundation for the firm’s monetization initiatives and to launch new lines of business.
  • Data analytics related to soil, water and weather conditions and forecasting demand based on microeconomic data and optimize product life cycles, thus supporting local producers and brewers.
    • The expected impact is 1 bill $.

Sources: The above text is a creative synthesis elaborated from the following sources: Valerie Logan (CIOfromIDG); Nikhil Babar (DZone); Vinay Nathan (IIoTWorld).

These sources have been selected from a total of 14 articles on the subject matter. Which in turn are the result of sifting through 48 articles.


  • We are living a movement towards sharing use cases, data and insights between companies that are even competing in different markets.
  • Example: data generated by dataphones:
    • They obtain extra profit selling (so monetizing) the data they generate that interests many others, who pay for such data:
      • Where the people are.
      • What they buy.
      • At what time of the day.
      • What amount of money.
    • Using these data, Predictive Analytics can tell:
      • Where people will be
      • At what time
      • What they will want to buy, etc…
  • Technically, this data sharing is carried out through DataPools, Cristina Grosu, of Lentiq, tells us:
  • This is important as companies will stop growing the expensive and huge DataLakes.
  • Companies are not starting from scratch any more (as ISACA explains):
    • If the data the company needs is already located, stored and cleaned, why should it be necessary to do the same work again?
    • The first one to locate, clean and store the data will have the possibility to monetize it.
    • Anyway, that data would be necessary for its own business.
  • Currently, we must think in terms of “co-creation” of value:
    • The client must participate in the value chain, not just the firm by itself.
  • Digital Transformation of the value chain –says Stuart Rance, of com– implies that the interaction with the customer doesn’t finish with the sale of a product:
    • The subsequent interaction with the customer produces most of the value of the relationship.
    • Example: social networks monetize the data produced by users through their interactions, comments, contents…
      • But they offer a free product (the App) to their users.
  • The approach is not any more to consider that the company merely “provides the service or product” and the customer simply “buys it“…
    • More than that: they join in an ecosystem or network of value creation.
    • The more interaction, the more value and satisfaction the customer receives.
  • Retail businesses, banking, airlines, media…
    • All of them offer App’s in order to interact with users as much as possible.
    • The very interaction creates value for the customer…
    • And enables companies to monetize the data generated by that interaction.
  • The massive introduction of the cloud is bringing new scenarios regarding strategies for sharing infrastructure assets.
  • Digital Transformation also means speeding up access and technical management of the services we offer.
  • In a context increasingly dependent on technology, paradoxically, we have to ensure that technology is increasingly hidden from the users’ sight.
  • We have 3 possible scenarios —as can be concluded from information provided by ISACA (Information Systems Audit and Control Association)–:
    • SaaS (Software as a Service): it means the user only worries about using certain functionality…
      • Regardless of where it is deployed or how many technical resources it needs.
    • PaaS (Platform as a Service): it means that customers can adapt the capacity of the infrastructure as they need it…
      • Allocating resources to their processing or storage needs at every moment.


  • IaaS (Infrastructure as a Service): it means that customers can shape and modify resources in the infrastructure
    • With one difference: these resources are in the cloud and not in local data processing centers.
  • Any of these 3 approaches definitively discards any advantage that the CPDs used to have with respect to the infrastructure in the cloud.
  • We have to face the fact: some activities will not generate monetization due to automation, Artificial Intelligence or other Digital Transformation consequences.
  • Language translators, recording musicians, investigative journalists, photographers and people of other professions…
    • Are facing a downsizing in their career opportunities.
  • Before, these professions were represented as a “bell curve”:
    • The majority at a normal professional level.
    • A few people at a highly professional level.
    • And very few people –the best of the best– at the top.
  • Today’s representation of these professions is a “zip curve”:
      • A tiny number of professionals succeeding
      • And a vast majority flattened.

Sources: The above text is a creative synthesis elaborated from the following sources: Cristina Grosu (Lentiq); Randy Bean (Forbes); Stuart Rance (SysAid); ISACA.

These sources have been selected from a total of 18 articles on the subject matter. Which in turn are the result of sifting through 69 articles.


  • To make your data revenue-driving, do the following:
    • Analyze your customer data.
    • Take relevant actions based on the analysis.
    • Deliver these actions to your customers at the right moment.
  • To give you a revenue lift, the data should be precise, accurate and accessible:
    • Is it the right data? Data scientists are responsible to correlate the data in order to discover hidden insights valuable for the business.
    • Is the data right? Data curators are responsible to provide high quality data.
    • Is it the data we need to decide? Business Analysts are responsible to choose the relevant data depending on the subject or issue under discussion.
    • Is the data we need to decide quick enough? In a data driven company, every employee must determine the minimum amount of data to enable the decision-making process.
  • For safe and secure data monetization, every business should take a data monetization road map :
    • Be educated on compliance and regulatory requirements: we are responsible for managing and protecting the data, but the owner is the person being referred to by the data.
    • Treat different types of data in accordance with their specific legal requirements.
    • Keep the data prepared for Machine Learning (ML) usage:
      • Demolish your data silos
      • Keep your data cleansed…
      • Real-time updated
      • And prepared for ML Analysis.
    • Use all the channels, while learning and leveraging other channels to maximize the profit and minimize the risks.

in Digital Transformation, Economies of Learning are more powerful than Economies of Scale:

  • Companies have to invest in the creation, sharing and reuse of their digital assets.
  • These investments will result in curated data and analytic capabilities.
  • These Analytic Modules will reveal insights that lead to avoid waste in stocks, idle times, over-processing…
  • As a consequence, it’s more worth it to firstly invest in analytical processing to learn how to make physical processes more efficient, than merely investing in providing more and more resources to those processes.
  • To monetize Big Data streams effectively, companies might partner with organizations that:
    • Are well established in their traditional markets…
    • Have big reserves of data, market knowledge and
  • This implies positive consequences:
    • That expertise means you can trust those reserves of data:
      • So you will save money not having to implement more data-quality check on them.
    • As the company can save money in the quality improvement activities…
      • It should permit their data teams to build use case-specific projects…
      • This investment is aimed to solve business problems instead of technical (data related)
    • Companies can save even more money extracting part of those data from Open Data repositories.

sources: The above text is a creative synthesis elaborated from the following sources: John Cusano (Insurance Blog); Bill Schmarzo (Data Science Central); John Nash (; SCALE AGILE, INC.

These sources have been selected from a total of 15 articles on the subject matter. Which in turn are the result of sifting through 59 articles.